Efficient low complexity high throughput ldpc decoding method and optimization

ABSTRACT

A decoder and method for iteratively decoding of low-density parity check codes (LDPC) includes, in a code graph, performing check node decoding by determining messages from check nodes to variable nodes. In the code graph, variable node decoding is performed by determining messages from the variable nodes to the check nodes. The variable node decoding is independent from degree information regarding the variable nodes. Decoded results are output.

RELATED APPLICATION INFORMATION

This application claims priority to provisional application Ser. No.60/974,512 filed on Sep. 24, 2007, incorporated herein by reference.

BACKGROUND

1. Technical Field

The present invention relates to decoding systems and methods and moreparticularly to low-density parity-check codes which employ lessinformation but provide improved performance.

2. Description of the Related Art

With capacity approaching decoding performance, low-density parity-checkcodes (LDPC) have drawn significant interest in many industries for thetransmission of digital information, including cellular systems, digitalrecording systems, etc. While turbo codes have been adopted in 3Gcellular standards, LDPC codes have been included in the IEEE 802.16standard and also considered for potential application beyond 3Gcellular standards, e.g., in 4G systems. Beside cellular systems, LDPCis also considered in digital video broadcasting (DVB) systems.

When applied to different systems, the requirements for decodingcomplexity and decoding throughput for LDPC codes are different. Softdecoding provides capacity approaching performance but has related highdecoding complexity. The binary message-passing decoding (or Harddecoding), although it incurs some performance loss, has verylow-complexity and high decoding throughput. Existing binary decodingalgorithms include a majority decoding algorithm (i.e., time-invariantdecoding, e.g., Majority Based (MB) decoding), and switch type decoding(e.g., Gallager decoding algorithm B (GB)) (time-variant decoding).These existing binary decoding methods require the degree information ofvariable nodes. To make the degree information available to the decoder,much additional complexity is introduced on a circuit implementation ofthe LDPC decoder.

Therefore, a need exists for a new binary message-passing LDPC codingthat does not need degree information and provides the same or betterperformance than existing systems.

SUMMARY

A decoder and method for iteratively decoding of low-density paritycheck codes (LDPC) includes, in a code graph, performing check nodedecoding by determining messages from check nodes to variable nodes. Inthe code graph, variable node decoding is performed by determiningmessages from the variable nodes to the check nodes. The variable nodedecoding is independent from degree information regarding the variablenodes. Decoded results are output.

A decoder for low-density parity check codes (LDPC) includes a checknode decoder configured to determine messages from check nodes tovariable nodes in a code graph, and a variable node decoder configuredto determine messages from the variable nodes to the check nodes. Thevariable node decoder decodes variable nodes independently from degreeinformation of the variable nodes.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 is a block/flow diagram showing an illustrative iterativedecoding system/method in accordance with the present principles;

FIG. 2 is a semi-log graph of bit error rate (BER) performance versusprobability (P₀) for time-invariant decoding for irregular (irr.) LDPCcodes with low error-floor (LF), regular (9,15) MB code is shown forcomparison in accordance with the present principles;

FIG. 3 is a semi-log graph of bit error rate (BER) performance versusprobability (Pa) for time-variant decoding for optimized irregular(irr.) LDPC codes with varying code rates (R=0.4, 0.5, and 0.6) inaccordance with the present principles; and

FIG. 4 is a block diagram showing a LDPC decoder in accordance with oneillustrative embodiment.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

In accordance with the present principles, a new binary message-passinglow-density parity-check code (LDPC) coding method with low-complexityand high decoding throughput is provided, which facilitates a circuitimplementation of a decoder. With existing binary message-passingdecoding methods, the complexity of circuit implementation is muchhigher than expected due to degree information required for the decoder.The present embodiments provide a novel decoding principle, which doesnot need degree information. Therefore, both computational complexityand circuit implementation complexity are very low.

With new decoding methods applied to both time-invariant and timevariant decoding methods, optimized irregular LDPC codes perform exactlythe same or better than the code from existing decoding methods. Thismeans the present decoding methods do not lose anything compared withthe existing methods, and yet gain based on low-complexity for thecircuit implementation. In addition, an estimation method is provided toestimate an extrinsic error probability for time-variant decoding. Thisissue has been overlooked in the prior art.

Binary message-passing decoding, i.e., hard decoding is provided thathas extremely low-complexity, which is achieved with little performanceloss as compared with soft decoding. This makes the decoding methods inaccordance with the present principles more attractive to many systemsthat demand high decoding throughputs.

A novel message-passing decoding method for low-density parity-checkcodes with low-complexity and high decoding throughput does not need thedegree information of the variable nodes and can be applied to bothtime-invariant and time-variant decoding methods. For time-variantdecoding, a method is presented to estimate the extrinsic errorprobability. EXIT chart analysis and code optimization for new decodingmethod in both time-invariant and time-variant decoding scenarios wereperformed. For time-invariant decoding, the optimized codes exhibit ahigh error floor in a high SNR region. A code design method achieves lowerror floor by introducing additional constraints. For time-variantdecoding, we show that the code optimization can be simplified with onlyseveral switching points in the constraints. The analysis and numericalresults show that the present methods provide the same performance asthe existing methods for both time-invariant and time-variant decodingwhile the new method achieves much lower complexity for circuitimplementations.

Embodiments described herein may be entirely hardware, entirely softwareor including both hardware and software elements. In a preferredembodiment, the present invention is implemented in hardware but mayinclude software, which includes but is not limited to firmware,resident software, microcode, etc.

Embodiments may include a computer program product accessible from acomputer-usable or computer-readable medium providing program code foruse by or in connection with a computer or any instruction executionsystem. A computer-usable or computer readable medium may include anyapparatus that stores, communicates, propagates, or transports theprogram for use by or in connection with the instruction executionsystem, apparatus, or device. The medium can be magnetic, optical,electronic, electromagnetic, infrared, or semiconductor system (orapparatus or device) or a propagation medium. The medium may include acomputer-readable medium such as a semiconductor or solid state memory,magnetic tape, a removable computer diskette, a random access memory(RAM), a read-only memory (ROM), a rigid magnetic disk and an opticaldisk, etc.

Due to relative large block-length usually considered for LDPC codes,maximum likelihood (ML) decoding methods, e.g., syndrome decoding,cannot be applied for decoding LDPC codes. Therefore, suboptimaldecoding methods, i.e., iterative message-passing decoding methods, areconsidered as the applicable decoding methods. Based on bipartite orTanner graph representations of LDPC codes, the iterativemessage-passing method performs the repetition decoding at variablenodes and single parity-check (SPC) decoding at check nodes iteratively.The extrinsic messages from the decoding at the variable nodes and checknodes are exchanged along edges in a code graph.

There are three categories of message-passing decoding methods, namely,soft decoding, hard decoding, and mixed decoding methods. With softdecoding methods, the decoder performs the iterative decoding byexchanging the soft extrinsic information between the variable nodes andcheck nodes. Such decoding methods, for example, sum-product decoding,provide better error correction performance but have high decodingcomplexity. Hard decoding, e.g., majority decoding, exchanges the binaryextrinsic message in the decoder. Such decoding methods have someperformance loss compared with soft decoding methods, but are attractivefor high throughput performance and low-complexity.

Mixed decoding, e.g., gear-shift decoding, reduces the computationalcomplexity of soft decoding without incurring the performance loss bydynamically switching the soft decoding and the binary decoding duringthe decoding iterations. Although the computational complexity isreduced, the implementation complexity in the circuit level is increaseddue to complex control units for the decoding switches and more decodingprocessing units for several types of decoders.

Referring now to the drawings in which like numerals represent the sameor similar elements and initially to FIG. 1, a new binarymessage-passing decoding principle is described, which focuses on binarymessage-passing hard decoding for LDPC codes.

We consider an irregular LDPC code with N variable nodes and M checknodes. We treat the (d_(v), d_(c)) regular LDPC codes as the specialcases of irregular codes. Assume that the channel is binary symmetricchannel (BSC) and the output is mapped to (+1, −1). Denote u_(c→v,j),v_(v→c,j), and u_(n) as the extrinsic message outputs from a check nodeto a variable node, an extrinsic message output from a variable nodepassed along the jth edge to a check node, and a channel message fromthe nth variable node, respectively. Define v_(n) as a set of edges thatare connected to the nth variable node, and U_(m) as a set of edges thatare connected to the mth check node. Note that any channel can betreated as BSC since we can always perform a hard decision from thechannel output or the equalizer output before performing the LDPCdecoding.

For regular codes, we have the cardinalities of v_(n) and U_(m) given by|v_(n)|=d_(v), n=1, . . . , N and |U_(m)|=d_(c), m=1, . . . , M,respectively.

The decoding output from the lth iteration (denoted by script 1 in theequations) is given by:

In block 102, check node decoding is performed in accordance with thefollowing equation:

${u_{{c\rightarrow v},k}^{(l)} = {\prod\limits_{{j \in U_{m}},{j \neq k}}v_{{v\rightarrow c},j}^{(l)}}},{k \in U_{m}},{m = 1},{\ldots \mspace{11mu} {M.}}$

For all existing binary message-passing decoding methods, the decoderhas to obtain the threshold values based on degree information of thevariable node. Degree is the weight of a coded bit (i.e., variable node)or a parity-check constraint (i.e., check node). When the code isrepresented by a code graph, the degree of a node (either a variablenode or a check node) is the number of edges connected to this node. Thedegree values of the nodes in the code graph are the degree information.

However, when implementing the decoder at the circuit level, the degreeinformation of the variable nodes is usually not available forprocessing unit in the LDPC decoder, or to implement such decodingmethods, the degree information for the variable nodes has to be storedin a circuit and retrieved by a processing unit. The implementationcomplexity of the LDPC decoder is then increased. Since the advantage ofLDPC hard decoding is low-complexity, the requirement of degreeinformation will weaken the advantages of the hard decoding.

In block 104, variable node decoding is performed without the need fordegree information in accordance with the following:

In block 106, we first obtain the cost m_(k), which is the discrepancyof extrinsic inputs:

${m_{k}^{()} = {- {\sum\limits_{{j \in V_{n}},{j \neq k}}{u_{{c - v},j}^{()}u_{n}}}}},{k \in {V_{n}.}}$

Then, we obtain the decoding principle or rule for variable nodes inblock 108:

$v_{{v->c},k}^{({ + 1})} = \left\{ \begin{matrix}{- u_{n}} & {{{{if}\mspace{14mu} m_{k}^{()}} \geq d^{\prime}},} \\{u_{n},} & {{otherwise},}\end{matrix} \right.$

where d is a flipping threshold for the decoding of variable nodes.Majority vote may be applied to the decoding of the variable nodes.Otherwise, channel input is sent as the extrinsic output.

One variation of obtaining the cost m_(k) is given by:

${{{For}\mspace{14mu} j} \in V_{n}},{j \neq {k\text{:}\mspace{14mu} \left\{ \begin{matrix}{{m_{k}^{()}++}.} & {{{{if}\mspace{14mu} u_{{c->v},j}^{()}} \neq u_{n}},} \\{{m_{k}^{()}--},} & {{{if}\mspace{14mu} u_{{c->v},j}^{()}} = {u_{n}.}}\end{matrix} \right.}}$

The cost may be obtained using other methods as well.

One important property of above decoding method is that unlike theexisting methods, the cost m_(k) does not need degree information of thevariable nodes, neither does the flipping threshold d. A mapping betweena threshold value (ω) in the existing MB decoding for bit flipping, andthe flipping threshold d in the present decoding method is possible, orthe flipping threshold d may be set as a function of the degrees ofvariable nodes. In a preferred embodiment, d is constant or uniform forall variable nodes to provide low-complexity.

Because the cost m_(k) is the discrepancy of the extrinsic inputs, thedegree information is not needed for a given d. This is applicable totime-variant (variable d) and time-invariant (fixed d) decoding methods.With this property, the complexity is significantly reduced for acircuit implementation of the decoder. For example, by reducing thenumber of inputs to a decoder design, which would have to be providedfor each decoded channel the complexity of the decoder design, issignificantly reduced.

In block 110, the present principles can be applied to irregular codeswith time-invariant decoding and time-variant decoding. When applied toirregular codes, the flipping threshold d is applied uniformly for allvariable nodes. In time-invariant decoding, d does not change during thedecoding. In time-variant, we change d^((l)) uniformly at the lthdecoding iteration.

In block 112, estimation of extrinsic error probability for decoding isperformed. Denote P_(c) ^((l)) as the probability of unsatisfied checknodes at the lth decoding iteration. The estimated extrinsic errorprobability for the LDPC codes with regular profiles for the check nodesis given by

${\hat{p}}^{()} = {\frac{1}{2}{\left( {1 - \left( {1 - {2{\hat{P}}_{c}^{()}}} \right)^{\frac{1}{d_{c}}}} \right).}}$

For the codes with irregular check nodes, the estimations of extrinsicerror probability with first order and second order approximations aregiven respectively, by:

$\begin{matrix}{{{\hat{p}}^{()} \approx \frac{{\hat{P}}_{c}^{()}}{\sum\limits_{j}{{\overset{\sim}{\rho}}_{j}j}}},} \\\left. \begin{matrix}{{\overset{\sim}{p}}^{()} \approx \frac{{\sum\limits_{j}{{\overset{\sim}{\rho}}_{j}j}} - \sqrt{\left( {\sum\limits_{j}{{\overset{\sim}{\rho}}_{j}j}} \right)^{2} - {4{\hat{P}}_{c}^{()}{\sum\limits_{j}{{\overset{\sim}{\rho}}_{j}{j\left( {j - 1} \right)}}}}}}{2{\sum\limits_{j}{{\overset{\sim}{\rho}}_{j}{j\left( {j - 1} \right)}}}}} \\{= {\frac{1}{2}{\frac{\sum\limits_{j}{{\overset{\sim}{\rho}}_{j}j}}{\sum\limits_{j}{{\overset{\sim}{\rho}}_{j}{j\left( {j - 1} \right)}}}\left\lbrack {1 - \sqrt{1 - {4{\overset{\sim}{P}}_{c}^{()}\frac{\sum\limits_{j}{{\overset{\sim}{\rho}}_{j}{j\left( {j - 1} \right)}}}{\left( {\sum\limits_{j}{{\overset{\sim}{\rho}}_{j}j}} \right)^{2}}}}} \right\rbrack}}} \\{{\approx {a\; {{\hat{P}}_{c}^{()}\left\lbrack {1 + {{\hat{P}}_{c}^{}b} + {2\left( {\hat{P}}_{c}^{()} \right)^{2}b^{2}}} \right\rbrack}}},}\end{matrix} \right|\end{matrix}$ where${a = \frac{1}{\sum\limits_{j}{{\overset{\sim}{\rho}}_{j}j}}},{b = \left. {\frac{\sum\limits_{j}{{\overset{\sim}{\rho}}_{j}{j\left( {j - 1} \right)}}}{\left( {\sum\limits_{j}{{\overset{\sim}{\rho}}_{j}j}} \right)^{2}}.} \right|}$

The estimation with the second order performs well in the time-variantdecoding.

Blocks 102-112 are part of an iterative decoding method and areperformed, as needed, in an iterative process. Therefore, the decoderiteratively performs check decoding and variable node decoding. Theiterative decoding stops if the maximum number of iterations is reached,or all parity checks are satisfied.

In block 116, code optimization may optionally be performed. The codeoptimization is not a part of the decoder. The code optimization finds abest code profile (of a group of codes, so called code ensemble) withthe best performance given a certain decoding method.

The ensemble of irregular LDPC codes can be specified by two polynomials

${{\lambda (x)} = {{\sum\limits_{j = 1}^{D_{L}}{\lambda_{j}x^{j - 1}\mspace{14mu} {and}\mspace{14mu} {\rho (x)}}} = {\sum\limits_{j = 1}^{D_{R}}{\rho_{j}x^{j - 1}}}}},$

where λ_(j) is the fraction of edges in the bipartite graph that areconnected to variable nodes of degree j, p_(j) is the fraction of edgesthat are connected to check nodes of degree j, D_(L) and D_(R) denotethe maximal degree of variable nodes and check nodes, respectively.Equivalently, the degree profiles can also be specified from the nodeperspective, i.e., two polynomials of the same form where λ_(j) is thefraction of variable nodes of degree j, and p_(j) is the fraction ofcheck nodes of degree j.

Time-invariant decoding: For time-invariant decoding, we simply applythe same threshold value d for all the variable nodes with differentdegree. For time-variant decoding we change d^((l)) similar to b_(j)^((l)). Again, we apply the same d^((l)) to all variable nodes in thenth iteration. Denote p₀ as the initial error probability from thechannel.

The irregular code optimization for time-invariant decoding with thepresent decoding principle is summarized as follows:

max_({λ_(j), ρ_(j), d, D_(min)})  p₀${{s.t.\mspace{14mu} {\sum\limits_{j = D_{\min}}^{D_{L}}{\lambda_{j}{h_{j,{\lfloor{{({j + d})}/3}\rfloor}}^{p_{0}}(x)}}}} < x},{{\forall x};{1 \leq d \leq \left( {D_{\min} - 1} \right)};}$${{and}\mspace{14mu} R} = {1 - {\frac{\sum\limits_{j}{\rho_{j}/j}}{\sum\limits_{j}{\lambda_{j}/j}}.}}$

where D_(min) denotes the minimal degree of variable nodes and checknodes and h^(p0) is the output extrinsic error probability for degree-jvariable nodes after one decoding iteration with extrinsic probability xwith the flipping threshold (j+d)/2.

As shown in Table 1, the performance of the optimized codes for newdecoding method is exactly the same as that for hybrid MB decoding, eventhough compared with code design for existing hybrid MB methods intime-invariant decoding, additional constraints are needed to beintroduced in the above optimization.

We find that the thresholds of designed codes for both decoding methodsare exactly the same for all the code rates, indicating there is noperformance loss. The components of hybrid MB decoding in optimizedcodes are given in the fourth column in Table 1. The optimized d*,D_(min) for new decoding method are given in the fifth column ofTable 1. It is seen that all the optimized components for hybrid MBdecoding can be mapped to the new decoding, which means that all theoptimized code profiles for hybrid MB decoding can be implemented withnew decoding method. This explains why the performance of optimizedcodes is exactly the same for both decoding methods.

To achieve a low-error floor, a modified code optimization is providedand summarized as follows

${{\sum\limits_{j = d_{t_{\min}}}^{d_{t_{\max}}}{\lambda_{j}{h_{j,{\lfloor{{({j + d})}/2}\rfloor}}^{p_{0}}(x)}}} < x},{{\forall x};{1 \leq d \leq {\left( {d_{t_{\min}} - 3} \right).}}}$

where d_(1min) and d_(1max) are the maximum and minimum flippingthresholds at the lth iteration.

TABLE 1 Decoding thresholds and degree components of optimized LDPCcodes for time-invariant decoding. Hybrid MB New New Code DecodingDecoding Hybrid MB Decoding Decoding Rate p₀* p₀* {MB_(j) ^(w)} d*,D_(min) R = 0.1, 0.16093 0.16003 {MB₃ ⁰, MB₃₀ ¹} 2, 3 R = 0.2, 0.123080.12308 {MB₃ ⁰, MB₃₀ ¹} 2, 3 R = 0.3, 0.09011 0.09011 {MB₃ ⁰, MB₄ ¹} 2,3 R = 0.4, 0.06938 0.00938 {MB₃ ⁰, MB₄ ¹, MB₀ ¹} 2, 3 R = 0.5, 0.052650.05265 {MB₄ ¹, MB₇ ¹, MB₂₉ ¹} 3, 4 R = 0.6, 0.03758 0.03758 {MB₃ ⁰, MB₄¹, MB₀ ¹} 2, 3 R = 0.7, 0.02349 0.02349 {MB₄ ¹, MB₅ ¹} 3, 4 R = 0.8,0.01407 0.01407 {MB₅ ¹, MB₇ ¹, MB₈ ²} 4, 5 R = 0.9, 0.00564 0.00564 {MB₅¹, MB₈ ², MB₁₆ ²} 4, 5

Time-variant decoding: For time-variant decoding, new decoding methodwith optimal switching is exactly the same as optimal GB decoding. Theirregular code optimization for time-variant decoding with the newdecoding principle can be simplified as follows:

${\max\limits_{\{{\lambda_{j},\rho_{j}}\}}p_{0}},{{s.t.\mspace{14mu} {h^{p_{0}}\left( v_{d}^{p_{0}} \right)}} < v_{d}^{p_{0}}},\left. {\forall{v_{d} < {p_{0}.}}} \right|$

The decoding of irregular code for time-variant decoding with optimalswitching points is the same as that of GB decoding. Therefore, theoptimized codes also perform the same as the GB decoding.

In block 114, the decoder(s) output the decoded results. Performanceresults comparisons for the decoders in accordance with the presentprinciples versus the conventional decoding scheme of MB are describedhereinafter.

As a point of comparison, MB decoding (time-invariant decoding) issummarized as follows:

Initialization:  u_(c− > v, k)⁽⁰⁾ = null, ∀k; v_(c− > c, k)⁽⁰⁾ = u_(n).  k ∈ V_(n)${{Check}\mspace{14mu} {node}\mspace{14mu} {decoding}\text{:}\mspace{14mu} u_{{c->v},k}^{()}} = {\prod\limits_{{j \in U_{m}},{j \neq k}}\; \begin{matrix}{v_{{v->c},j}^{()},{k \in U_{m}},} \\{{m = 1},{\ldots \mspace{11mu} {M.}}}\end{matrix}}$${{Variable}\mspace{14mu} {node}\mspace{14mu} {decoding}\text{:}\mspace{14mu} v_{{v->c},k}^{({ + 1})}} = \left\{ \begin{matrix}{{- u_{n}},} & {{if}\mspace{14mu} \begin{matrix}{{\begin{Bmatrix}{{{j:u_{{c->v},j}^{()}} = {- u_{n}}},} \\{{j \in V_{n}},{j \neq k}}\end{Bmatrix}} \geq} \\{\left\lceil {{V_{n}}/2} \right\rceil + \omega}\end{matrix}} \\{u_{n}.} & {{otherwise}.}\end{matrix} \right.$

The GB decoding (time-variant decoding) method is summarized as follows:The variable node decoding is given by;

$u_{{v->c},k}^{({ + 1})} = \left\{ \begin{matrix}{- u_{n}} & {{{if}\mspace{14mu} {\left\{ {{{j:u_{{c->v},j}^{()}} = {- u_{n}}},{j \in V_{n}},{j \neq k}} \right\} }} \geq b_{n}^{({ + 1})}} \\{u_{n},} & {{otherwise},}\end{matrix} \right.$

where b _(n) ^((l)) ,┌|V _(n)|/2┐≦b _(n) ^((l)) ≦|V _(n)|−1,| denotesthe flip threshold in decoding the nth variable node at the lth decodingiteration.

Both existing methods for variable decoding require the degreeinformation of the variable nodes, which increases the complexity of thecircuit implementation.

Performance results: The optimized codes for decoding methods inaccordance with the present principles in time-invariant decoding haveexactly the same performance as the existing hybrid MB decoding.

TABLE 2 Number of component EXIT functions for code optimization withtime-invariant decoding. Hybrid MB Decoding  Number of componentEXITfunctions${\sum\limits_{j = D_{\min}}^{D_{L}}\; j} - \left\lbrack {j/2} \right\rbrack$ New DecodingD_(L) − D_(min) D_(L) = 30, D_(min) = 3 224 28 D_(L) = 50,D_(min) = 3 624 48

Advantageously, the number of EXIT functions is significantly reduced inaccordance with the present principles.

FIG. 2 is a semi-log graph of bit error rate (BER) performance versusprobability (P₀) for time-invariant decoding for irregular (irr.) LDPCcodes with low error-floor (LF), regular (9,15) MB code is shown forcomparison. R is the code rate. Here in FIG. 2, the code rate R=0.4.

FIG. 3 is a semi-log graph of bit error rate (BER) performance versusprobability (P₀) for time-variant decoding for optimized irregular(irr.) LDPC codes with varying code rate R (R=0.4, 0.5, and 0.6) inaccordance with the present principles.

Referring to FIG. 4, an iterative decoder 200 for low-density paritycheck codes (LDPC) is preferably implemented in a hardware circuit. Thehardware circuit may include software elements and may be formed on aprinted wiring board, integrated circuit chip or embodied in any othertype of hardware. Decoder 200 includes a check node decoder 202configured to determine messages from check nodes to variable nodes in acode graph 207. A variable node decoder 204 is configured to determinemessages from the variable nodes to the check nodes wherein the variablenode decoder decodes variable nodes independent from degree informationof the variable nodes.

The variable node decoder 204 includes flipping thresholds 205 which arenot based upon degree information of the variable nodes as describedabove with respect to FIG. 1. The variable node decoder finds a costm_(k) which is determined from a discrepancy between extrinsic inputs ofvariable nodes. The cost m_(k) may be computed as

${m_{k}^{(l)} = {- {\sum\limits_{{j \in V_{n}},{j \neq k}}{u_{{c\rightarrow v},j}^{(l)}u_{n}}}}},{k \in {V_{n}.}}$

where u_(c→v,j) is the extrinsic message outputs from a check node alonga jth edge to a variable node, v_(v″c,j), is an extrinsic message outputfrom a variable node passed along a jth edge to a check node, u_(n) is achannel message from the nth variable node, v_(n) is a set of edges thatare connected to the nth variable node, and k is an index of the edgesin the code graph. In the equation, for the particular node n, k is theindex of the edges that are connected to node n, i.e., kεν_(n).

The variable node decoder includes a decoding principle for variablenodes, e.g.,

$v_{{v\rightarrow c},k}^{({l + 1})} = \left\{ \begin{matrix}{- u_{n}} & {{{{if}\mspace{14mu} m_{k}^{(l)}} \geq d},} \\{u_{n},} & {{otherwise},}\end{matrix} \right.$

where v_(v→c,j), is an extrinsic message output from a variable nodepassed along a kth edge to a check node, u_(n) is a channel message fromthe nth variable node, and d is a flipping threshold for the decoding ofvariable nodes. Other decoding principles or rules may also be employed.

Having described preferred embodiments of a system and method forefficient low complexity high throughput LPDC decoding and optimization(which are intended to be illustrative and not limiting), it is notedthat modifications and variations can be made by persons skilled in theart in light of the above teachings. It is therefore to be understoodthat changes may be made in the particular embodiments disclosed whichare within the scope and spirit of the invention as outlined by theappended claims. Having thus described aspects of the invention, withthe details and particularity required by the patent laws, what isclaimed and desired protected by Letters Patent is set forth in theappended claims.

1. A method for iterative decoding of low-density parity check codes(LDPC), comprising: in a code graph, performing check node decoding bydetermining messages from check nodes to variable nodes; in the codegraph, performing variable node decoding by determining messages fromthe variable nodes to the check nodes wherein the variable node decodingis independent from degree information regarding the variable nodes; andoutputting decoded results.
 2. The method as recited in claim 1, whereinperforming variable node decoding includes obtaining flipping thresholdvalues for determining the results which are not based upon degreeinformation of the variable nodes.
 3. The method as recited in claim 1,wherein performing variable node decoding includes obtaining a costm_(k), which is a discrepancy between extrinsic inputs of variablenodes.
 4. The method as recited in claim 3, wherein the cost m_(k) isdetermined as${m_{k}^{()} = {- {\sum\limits_{{j \in V_{n}},{j \neq k}}{u_{{c->v},j}^{()}u_{n}}}}},{k \in {V_{n}.}}$where u_(c→v,j) is the extrinsic message outputs from a check node alonga jth edge to a variable node, v_(v→c,j), is an extrinsic message outputfrom a variable node passed along a jth edge to a check node, u_(n) is achannel message from the nth variable node, v_(n) is a set of edges thatare connected to the nth variable node, and k is an index of edges in acode graph.
 5. The method as recited in claim 1, wherein performingvariable node decoding includes providing a decoding principle forvariable nodes.
 6. The method as recited in claim 5, wherein providing adecoding principle for variable nodes includes$u_{{v->c},k}^{({ + 1})} = \left\{ \begin{matrix}{- u_{n}} & {{{{if}\mspace{14mu} m_{k}^{()}} \geq d},} \\{u_{n},} & {{otherwise}.}\end{matrix} \right.$ where v_(v→c,j), is an extrinsic message outputfrom a variable node passed along a kth edge to a check node, u_(n) is achannel message from the nth variable node, and d is a flippingthreshold for the decoding of variable nodes.
 7. The method as recitedin claim 6, wherein the flipping threshold is independent of degreeinformation for variable nodes.
 8. The method as recited in claim 6,wherein the flipping threshold is constant for time-invariant decodingmethods.
 9. The method as recited in claim 6, wherein the flippingthreshold is varied in accordance with decoding iteration fortime-variant decoding methods.
 10. A decoder for low-density paritycheck codes (LDPC), comprising: a check node decoder configured todetermine messages from check nodes to variable nodes in a code graph;and a variable node decoder configured to determine messages from thevariable nodes to the check nodes wherein the variable node decoderdecodes variable nodes independently from degree information of thevariable nodes.
 11. The decoder as recited in claim 10, wherein thevariable node decoder includes flipping thresholds which are not basedupon degree information of the variable nodes.
 12. The decoder asrecited in claim 10, wherein the variable node decoder includes a costMk which is determined from a discrepancy between extrinsic inputs ofvariable nodes.
 13. The decoder as recited in claim 12, wherein the costm_(k) is computed as${m_{k}^{()} = {- {\sum\limits_{{j \in V_{n}},{j \neq k}}{u_{{c->v},j}^{()}u_{n}}}}},{k \in {V_{n}.}}$where u_(c→v,j) is the extrinsic message outputs from a check node alonga jth edge to a variable node, v_(v→c,j), is an extrinsic message outputfrom a variable node passed along a jth edge to a check node, u_(n) is achannel message from the nth variable node, v_(n) is a set of edges thatare connected to the nth variable node, and k is an index of the edge inthe code graph.
 14. The decoder as recited in claim 10, wherein thevariable node decoder includes a decoding principle for variable nodes.15. The decoder as recited in claim 14r wherein the decoding principlefor variable nodes includes$u_{{v->c},k}^{({ + 1})} = \left\{ \begin{matrix}{- u_{n}} & {{{{if}\mspace{14mu} m_{k}^{()}} \geq d},} \\{u_{n},} & {{otherwise},}\end{matrix} \right.$ where v_(v→c,j), is an extrinsic message outputfrom a variable node passed along a kth edge to a check node, u_(n) is achannel message from the nth variable node, and d is a flippingthreshold for the decoding of variable nodes.
 16. The decoder as recitedin claim 15, wherein the flipping threshold is independent of degreeinformation for variable nodes.
 17. The decoder as recited in claim 15,wherein the flipping threshold is constant for time-invariant decodingmethods.
 18. The decoder as recited in claim 15, wherein the flippingthreshold is varied in accordance with decoding iteration fortime-variant decoding methods.
 19. The decoder as recited in claim 10,wherein the decoder is implemented in a hardware circuit.